Methods for detecting heart rate, respiration, and oxygen saturation and uses thereof

ABSTRACT

This invention generally relates to methods useful for measuring heart rate, respiration conditions, and oxygen saturation and a wearable device that incorporate those methods with a computerized system supporting data collection, analysis, readout and sharing. Particularly this present invention relates to a wearable device, such as a wristwatch or ring, for real time measuring heart rate, respiration conditions, and oxygen saturation.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present U.S. patent application relates to and claims the prioritybenefit of U.S. Non-Provisional application Ser. No. 16/159,007 filedOct. 12, 2018, now U.S. Pat. No. 10,786,201 to Linnes et al., whichclaims the priority benefit of U.S. Provisional Patent Application Ser.No. 62/571,299, filed Oct. 12, 2017, the contents of each of which ishereby incorporated by reference in its entirety into the presentdisclosure.

GOVERNMENT SUPPORT CLAUSE

This invention was made with government support under grant DA038886,awarded by the National Institutes of Health. The government has certainrights in the invention.

TECHNICAL FIELD

This invention generally relates to a method useful for measuring heartrate, respiration conditions, and oxygen saturation and a wearabledevice that incorporate said method with a computerized systemsupporting data collection, analysis, readout and sharing.

BACKGROUNDS AND SUMMARY OF THE INVENTION

This section introduces aspects that may help facilitate a betterunderstanding of the disclosure. Accordingly, these statements are to beread in this light and are not to be understood as admissions about whatis or is not prior art.

Wearable devices are getting more popularity almost on daily basis,rapidly advancing in terms of technology, functionality, and size, withmore real-time applications. A wearable device, wearable technology, ora wearable gadget is a category of technology devices that can be wornby a consumer and often include tracking information related to healthand fitness. A wearable device as disclosed herein refers to awristwatch, a ring or a necklace. Any additional capabilities to thosedevice will add more value and enhance their popularity, not only toeveryday people on the street, but also to those sick and feeble in needof special cares. Additionally, a wearable device may also find uses inremote monitoring and diagnosis of patients' health conditions.

Photoplethysmography (PPG) is a simple, optical technique used to detectvolumetric changes in blood in peripheral circulation. It is a low-costand non-invasive method that makes measurements at the surface of theskin. PPG makes uses of low intensity infrared (IR) light. When lighttravels through biological tissues, it is absorbed by bones, skinpigments as well as venous and arterial blood. Since light is morestrongly absorbed by blood than other surrounding tissues, thevolumetric changes in blood flow can be detected by PPG sensors aschanges in the intensity of light. The voltage signal from PPG isproportional to the quantity of blood flowing through the blood vessels.Blood flow variations mostly occur

in the arteries, and not much in the veins. Other factors affecting therecordings from the PPG are the site of measurement, the contact forcebetween the site and the sensor, as well as the skin color at the siteof measurement.

The measurements provide valuable information related to thecardiovascular system and are widely used in clinical physiologicalmeasurements and monitoring, including heart rate and pulse oximetry.Existing PPG technologies apply sensors to the finger, but thistechnique suffer from lack of mobility. Numerous technologies can beworn on the wrist; however, they are unable to detect respiration orblood oxygenation levels. There is need of more practical method formonitoring health via PPG that can detect all needed measurements.

This invention generally relates to a method useful for measuring heartrate, respiration conditions, and oxygen saturation and a wearabledevice that incorporate those methods with a computerized systemsupporting data collection, analysis, readout and sharing. Particularlythis present invention relates to a wearable device, such as awristwatch, for real time measuring heart rate, respiration conditions,and oxygen saturation, wherein those data can be shared and distributedremotely.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the present disclosure will now be described by way ofexample in greater detail with reference to the attached figures, inwhich:

FIG. 1 shows a prototype of a wearable device disclosed herein.

FIG. 2A depicts the waveforms (top panel) the respiration signal inreal-time. The dark blue signal is the signal from the gold standard(impedance pneumograph, also known as a pneumatograph or spirograph),the light blue is the signal from our custom device, and the pink signalis also from our custom device after further amplification andfiltering. The bottom graph is the fast Fourier Transform (FFT) of thegold standard. This is used to analyze the frequency components of thesignal collected from the gold standard in order to calculaterespiratory rate. There is a primary frequency component at around 300mHz. Meaning the respiratory rate is about 0.3 Hz*60=18 breaths perminute.

FIG. 2B shows the FFT analysis is now of our custom device. We arecomparing the bottom graph of this slide to the bottom graph of slide 3.As you note, there is a primary frequency component at around 300 mHz.This primary frequency component of our custom device (bottom graphslide 4) is identical to the primary frequency component of the goldstandard (bottom graph slide 3) meaning that our device was able todetect respiration as well as the gold standard.

FIG. 2C shows scaled up respiratory patterns as detected by the goldstandard (in dark blue), our device (in light blue), and our deviceagain after further amplification and filtering (pink). The sinusoidalpatterns measured by our device matches the gold standard for normalbreathing, short choppy breaths, and holding one's breath. This slidedemonstrates that our device is also able to detect breathing patternssimilar to the gold standard.

FIG. 2D shows scanned images of subjects' wrists. Images were taken withan Epson Perfection V850 Pro using the Epson Scan Windows applicationVersion 3.9.3.2US with 24-bit color and 600 dpi resolution.

FIG. 3A shows the time domain response of the experimental device toheartbeats for Subjects A and B. We can visibly observe at least 2frequency components in each signal, one possibly corresponding to heartrate and the other to respiration.

FIG. 3B shows each signal is processed in the frequency domain.Suspected frequency components indicative of heart rate are highlighted.

FIG. 3C shows data from the reference device indicates heart rates of 83BPM and 73 BPM for Subjects A and B, respectively. These ratescorrespond to frequency components 1.40 Hz for Subject A and 1.21 Hz forSubject B.

FIG. 3D shows the respiratory signal is shown. We notice slower changesin the signal on the order of several seconds compared to heart rate,which was more on the order of I to 1.5 seconds.

FIG. 3E shows the spectra of the respiration signals are highlightedwith two dominant frequency components for each Subject (0.12 Hz and0.28 Hz for Subject A and 0.12 Hz and 0.25 Hz for Subject B).

FIG. 3F shows data from the reference device indicates a respiratoryrates of 16.1 breathes per minute (BrthPM) for Subject A and 15.3 Hz forSubject B. These rates correspond to peaks at 0.28 Hz (Subject A) and0.25 Hz (Subject B) for each respective subject.

FIG. 4A shows amplitude increase in signal due to activity of auto-gainfunction. The microcontroller samples the dataset for the full samplingperiod, then adjusts the gain of the active filters to module signalamplitude.

FIG. 4B shows real-time, on-board signal processing of heart andrespiratory signals after a very stressful event. The refresh rate ofthe display is such that heart rate (HR) and respiratory rate (RR) couldnot be captured in a single photograph at the same time.

Additionally, there is a few seconds delay between each snapshotresulting in the difference in HR and RR reported by the referencedevice (viewed on the smartphone), which has a faster refresh rate thanthe experimental device.

FIG. 5A shows our schematic for our biometric sensor board. In thisdesign, we depict our amplification and filtering scheme for measuringheart rate, respiration, and pulse oximetry. We have a dual LED (red andinfrared) in which we are able to automatically control the brightnessof the LEDs to account for difference in skin tones.

FIG. 5B shows schematic of the photoplethysmography (PPG) sensingcircuit. The signal from the transimpedance amplifier is sent into twosets of cascaded active filters tuned for heart rate sensing andrespiration monitoring respectively. The outputs of the amplifier stagesare sensed by two channels of an on-board 10-bit analog-to-digitalconverter on a Bluetooth-enabled microcontroller. The signals areprocessed by the on-board microcontroller. R3, R14, and R15 (highlightedwith a dashed box) are digitally controlled potentiometers enablingautomatic gain control. Using these potentiometers, the microcontrollercan modulate system gain in order to adjust for differences in theoptical reflective properties of skin across difference subjects.

FIG. 6 depicts the circuit board design for our biometric sensor boardshowing how the circuit board is physically laid out in space. Along theleft side are the amplifiers and filters. At the center and bottom ofthe board are the LEDs and light sensor. At the top is the brightnesscontrol mechanism for the LEDs.

FIG. 7 shows the back side of the circuit board.

FIG. 8 shows This slide shows our auxiliary sensor board that containsbattery management systems (voltage regulation, battery charging,battery status), a locomotion sensor (accelerometer and gyroscope), areal-time clock (for accurate time keeping), a motor (for userfeedback), and a buzzer (also for user tactile feedback).

FIG. 9 shows the circuit board layout for the auxiliary sensor board andwhere all the components are laid out.

FIG. 10 shows the communications and central processing board thatcontains our Bluetooth capable central processing unit, an externalmemory chip (RAM), a screen for displaying information to the user(OLED), an SD card for long term data storage, and buttons for userinput. This board handles all the data processing to measure heart rate,respiratory rate, and pulse oximetry. This board is also responsible forcontrolling all other boards and for sending data to the user.

FIG. 11A depicts the circuit board layout for our communications andcentral processing board.

FIG. 11B shows the back side of the circuit board.

DETAILED DESCRIPTION

For the purposes of promoting an understanding of the principles of thepresent disclosure, reference will now be made to the embodimentsillustrated in the drawings, and specific language will be used todescribe the same. It will nevertheless be understood that no limitationof the scope of this disclosure is thereby intended.

In the present disclosure the term “about” can allow for a degree ofvariability in a value or range, for example, within 20%, within 10%,within 5%, or within 1% of a stated value or of a stated limit of arange.

In the present disclosure the term “substantially” can allow for adegree of variability in a value or range, for example, within 80%,within 90%, within 95%, or within 99% of a stated value or of a statedlimit of a range.

This invention generally relates to a method useful for measuring heartrate, respiration conditions, and oxygen saturation and a wearabledevice that incorporate those methods with a computerized systemsupporting data collection, analysis, and readout. Particularly thispresent invention relates to a wearable device, such as a wristwatch, aring or a necklace, for real time measuring heart rate, respirationconditions, and oxygen saturation.

In some illustrative embodiments, the invention relates to a method formeasuring heart rate, respiration, and oxygen saturation of a subjectcomprising extracting and processing photoplethysmographic (PPG) datafrom said subject and displaying a readout on a wearable device.

In some illustrative embodiments, the invention relates to a method formeasuring heart rate, respiration, and oxygen saturation of a subjectwith a single device comprising the step of

-   -   a) measuring reflectance of red and infrared lights off skin        tissue using a biometric sensor board;    -   b) processing and analyzing data collected in step a); and    -   c) displaying and/or sharing results of the processed data        remotely.

In some illustrative embodiments, the invention relates to a method formeasuring heart rate, respiration, and oxygen saturation of a subjectwith a single device as disclosed herein, wherein the subject is a humanpatient or a healthy subject in need of monitoring for safety purpose.

In some illustrative embodiments, the invention relates to a method formeasuring heart rate, respiration, and oxygen saturation of a subjectwith a single device as disclosed herein, wherein said device is awearable device comprising a watch, a ring or a necklace.

In some illustrative embodiments, the invention relates to a method formeasuring heart rate, respiration, and oxygen saturation of a subjectwith a single device as disclosed herein, wherein said biometric sensorboard extracts and collects photoplethysmographic data from saidsubject.

In some illustrative embodiments, the invention relates to a method formeasuring heart rate, respiration, and oxygen saturation of a subjectwith a single device as disclosed herein, wherein said biometric sensorboard comprises a light sensor, a dual wavelength light source (red andinfrared) with an auto-gain feedback loop for calibrating skin tones andpulse strength, and a plurality of active filters for an enhanced signalto noise ratio with an auto-gain feedback loop to maintain a reasonablesignal strength for ease of signal processing.

In some illustrative embodiments, the invention relates to a method formeasuring heart rate, respiration, and oxygen saturation of a subjectwith a single device as disclosed herein, wherein said data processingand analyzing carried out by a microcontroller equipped with a centralprocessing unit comprises noises elimination from extractedphotoplethysmographic data using a motion tracking sensor for anenhanced signal to noise ratio.

In some illustrative embodiments, the invention relates to a method formeasuring heart rate, respiration, and oxygen saturation of a subjectwith a single device as disclosed herein, wherein said motion trackingsensor is an accelerometer, a gyroscope, or a combination thereof.

In some illustrative embodiments, the invention relates to a method formeasuring heart rate, respiration, and oxygen saturation of a subjectwith a single device as disclosed herein, wherein said data processingand analyzing comprises a frequency analysis using a Fourier Transformon data collected over several seconds of time, wherein said frequencyis reasonably related to a biometric measurement of heart rate andrespiration.

In some illustrative embodiments, the invention relates to a method formeasuring heart rate, respiration, and oxygen saturation of a subjectwith a single device as disclosed herein, wherein said reasonablefrequency for heart rate measurement ranges from about 0.4 Hertz (Hz) toabout 4 Hz; said reasonable frequency for respiration measurement rangesfrom about 0.05 Hz to about 1.2 Hz; and said heart rate and respirationare determined by comparing the magnitude of a peak of the FourierTransform for heart rate or respiration at each frequency to themagnitude of a peak in the Fourier Transform of theaccelerometer/gyroscope data in all three axes at each frequency,respectively.

In some illustrative embodiments, the invention relates to a method formeasuring heart rate, respiration, and oxygen saturation of a subjectwith a single device as disclosed herein, wherein said oxygen saturationin blood of said subject is determined by comparing the relative signalstrength of red and infrared light collected over several seconds oftime.

In some illustrative embodiments, the invention relates to a wearabledevice measuring heart rate, respiration and oxygen saturation of asubject can be displayed and/or shared remotely.

In some illustrative embodiments, the invention relates to a wearabledevice measuring heart rate, respiration and oxygen saturation of asubject can be displayed and/or shared remotely as disclosed herein,which further comprising a means for pinpointing the exact location ofsaid subject for monitoring and detecting drug use or abuse of saidsubject remotely.

In some illustrative embodiments, the invention relates to a wearabledevice for measuring heart rate, respiration, and oxygen saturation of asubject comprising

-   -   a) a biometric sensor board measuring reflectance of red and        infrared lights off skin tissue;    -   b) a microcontroller processing data collected by said        photosensor, wherein a dedicated memory chip is installed for        storing large amounts of data for real-time signal processing;    -   c) a power supply; and    -   d) a means for displaying and/or sharing results of the        processed data.

In some illustrative embodiments, the invention relates to a wearabledevice for measuring heart rate, respiration, and oxygen saturation of asubject in a single device further comprising a motion tracking sensorfor an enhanced signal to noise ratio by eliminating noises fromextracted photoplethysmographic data.

In some illustrative embodiments, the invention relates to a wearabledevice for measuring heart rate, respiration, and oxygen saturation of asubject in a single device further comprising a motion tracking sensorfor an enhanced signal to noise ratio by eliminating noises fromextracted photoplethysmographic data, wherein said motion trackingsensor is an accelerometer, a gyroscope or a combination thereof.

In some illustrative embodiments, the invention relates to a wearabledevice for measuring heart rate, respiration, and oxygen saturation of asubject in a single device further comprising a real-time clock foraccurate time keeping and a means for battery level monitoring.

In some illustrative embodiments, the invention relates to a wearabledevice for measuring heart rate, respiration, and oxygen saturation of asubject in a single device further comprising a means for pinpointingthe location of said subject for monitoring and detecting drug use orabuse of said subject.

In some illustrative embodiments, the invention relates to a wearabledevice for measuring heart rate, respiration, and oxygen saturation of asubject in a single device further comprising input and outputcapabilities for charging, programming, and data transfer and sharing.

In some illustrative embodiments, the invention relates to a wearabledevice for measuring heart rate, respiration, and oxygen saturation of asubject in a single device further comprising means for long term andshort term data storage.

In some illustrative embodiments, the invention relates to a wearabledevice for measuring heart rate, respiration, and oxygen saturation of asubject in a single device, wherein said biometric sensor boardcomprises a light sensor, a dual wavelength light source (red andinfrared) with an auto-gain feedback loop for calibrating skin tones andpulse strength, and a plurality of active filters for an enhanced signalto noise ratio with an auto-gain feedback loop to maintain a reasonablesignal strength for ease of signal processing.

In some illustrative embodiments, the invention relates to a wearabledevice for measuring heart rate, respiration, and oxygen saturation of asubject in a single device, wherein said heart rate, respiration, andoxygen saturation of a subject are measured, displayed and shared in asingle device.

In some illustrative embodiments, the invention relates to a wearabledevice for measuring heart rate, respiration, and oxygen saturation of asubject in a single device, wherein said heart rate, respiration, andoxygen saturation of a subject are shared and/or monitored remotely.

In some illustrative embodiments, the invention relates to a wearabledevice for measuring heart rate, respiration, and oxygen saturation of asubject in a single device, wherein said data processing and analyzingcomprises a frequency analysis using a Fourier Transform on datacollected over several seconds of time, wherein said frequency isreasonably related to a specific biometric measurement of heart rate andrespiration.

In some illustrative embodiments, the invention relates to a wearabledevice for measuring heart rate, respiration, and oxygen saturation of asubject in a single device, wherein said data processing and analyzingcomprises a frequency analysis using a Fourier Transform on datacollected over several seconds of time, wherein said frequency isreasonably related to a specific biometric measurement of heart rate andrespiration, and wherein said reasonable frequency for heart ratemeasurement ranges from about 0.4 Hertz (Hz) to about 4 Hz; saidreasonable frequency for respiration measurement ranges from about 0.05Hz to about 1.2 Hz; and said heart rate and respiration are determinedby comparing the magnitude of a peak of the Fourier Transform for heartrate and respiration at each frequency to the magnitude of a peak in theFourier Transform of the accelerometer/gyroscope data in all three axesat each frequency, respectively.

In some illustrative embodiments, the invention relates to a wearabledevice for measuring heart rate, respiration, and oxygen saturation of asubject in a single device, wherein said data processing and analyzingcomprises a frequency analysis using a Fourier Transform on datacollected over several seconds of time, and wherein said oxygensaturation in blood of said subject is determined by comparing relativesignal strength of red and infrared light collected over several secondsof time.

Methods to measure physiological signals, such as heart rate, usingphotoplethysmography (PPG) detect changes in the volume of blood flowingthrough blood vessels due to the rhythmic activity of the heart (J.Allen, Physiol. Meas. 2007, 28(3), p. R1). This volume change ismeasured by illuminating the capillary bed with a small light source andmeasuring the amount of light that reflects or passes through the tissuewith a photodiode. This technique has been utilized at length inconsumer fitness devices for continuous monitoring of heart rate duringrest and exercise (D K Spierer, et al., J. Med. Eng. Technol., 2015,39(5), 264-271). Though heart rate monitoring is undoubtedly beneficialfor monitoring general health and activity levels, more sensingcapabilities are needed to provide a more holistic picture of humanhealth. Of these additional sensing capabilities, respiration is ofparticular value since it provides a more comprehensive evaluation ofcardiopulmonary activity when coupled with heart rate monitoring (J FFieselmann, et al., J. Gen. Intern. Med., 1993, 8(7), 354-360).Respiratory monitoring provides additional clinical diagnosticcapabilities for diagnosing anomalies such as sleep apnea,hyperventilation, and panic disorders. As such, respiratory ratemeasurements have extensive clinical utility.

Conveniently, PPG has also been shown to measure respiratory signals inaddition to heart rate (P Leonard, et al., Emerg. Med. J. EMJ, 2003,20(6), 524-525; D Clifton, et al., J. Clin. Monit. Comput., 2007, 21(1),55-61). This suggests that there is a possibility of monitoringrespiratory and heart rate with a single device by analyzing the PPGsignal.

In addition to sensing capabilities and detection modalities, the choiceof form factor is critical. For heart rate measurements, both cheststrap and wrist-worn devices have been developed. However, chest strapsare known for their level of discomfort making a wrist-worn device amore attractive form factor. In this report, we detail our on-goingdevelopment of a wrist-worn PPG device capable of measuring both heartrate and respiration. Our device exceeds the abilities of currentcommercially available wrist-worn fitness devices, which are able tomeasure heart rate alone, by demonstrating the ability to measure bothheart rate and respiration using additional filtering and signalamplification strategies (D K Spierer, et al., 2015).

A. Feature-Packed

The device is fully-featured to include the main components to performthe physiological monitoring, as well as auxiliary features to give itthe capabilities of a watch. The device contains a 9 DoF inertialmeasurement unit, a PCF8523 real-time clock, SD card, 0.66in′ OLED,three user input buttons, an ARM Cortex MO Bluetooth microcontroller,external RAM, a vibration motor, a speaker, fuel gauge for monitoringbattery usage, battery charging circuit, and voltage regulator. Thesecomponents were designed on custom designed printed circuit boardscontained within a 42 mm wide×38 mm tall×18 mm tall 3D printedenclosure.

B. Pulse and Respiration Sensor Construction

The PPG sensing circuit (FIG. 5B) is primarily composed of a photodiode,a transimpedance amplifier (TIA), and two sets of cascaded activefilters. Each set of filters is specifically tuned for monitoring heartrate, which has a frequency range of 0.7 Hz to 3.5 Hz corresponding to42 beats per minute (BPM) to 210 BPM, and respiratory rate, which has afrequency range of 0.2 Hz to 0.5 Hz corresponding to 12 breaths perminute (BrthPM) to 30 BrthPM, in healthy human adults (E L Eckberg, etal., J. Physiol., 1980, 304(1), 489-502). The TIA converts the currentproduced by the photodiode to a voltage. This voltage is then sent intoeach respective set of active filters tuned for heart and respiratoryrate monitoring respectively. The outputs of our PPG sensing circuitsare sensed by a microcontroller with a 10-bit analog-to-digitalconverter. The heart rate circuit is sampled at 11.9 Hz, while therespiration circuit is sampled at 4.0 Hz satisfying Nyquist.

C. Automatic Gain Control

The signal from the photodiode will vary with a number of physiologicalfactors such as skin tone (J Kim, et al. Sci. Adv. 2016, 2(8),e1600418). As such, it is necessary to scale the gain of each amplifierstage to avoid saturating the amplifiers. To accomplish this, we employa dynamic gain control using digitally controlled potentiometers (R3,R14, and R15 in FIG. 5B). Digital potentiometers are variable resistorsthat can be programmed over a serial interface such as 4-bit I²Cbus/SMBus input/output expanders (sub as PCA9536 by NXP SemiconductorsN.V.). By employing these digital potentiometers, we can modify the gainof each amplifier stage in software, removing the need for userintervention, and allowing the device to be portable and used in thefield.

D. Comparison to Reference Standard

We compared the results from our experimental device to the BioHarness 3from Zephyr™ Technology. The BioHarness 3 is a U.S.-FDA clearedphysiological monitor available in the form of a chest strap. The devicecollects the electrocardiogram (ECG) signal using fabric electrodeslocated in the chest strap, and reports both heart and respiratory ratesto the user via a convenient mobile application. The data from theBioHarness was exported using the IoTool smartphone application on a ZTEWhirl 2 Z667G Android phone. We chose to compare our device to theBioHarness due to its portability, relative low-cost, and accessible appinterface. Furthermore, the device has been validated for accuracy in anearlier study (J H Kim, et al., Int. J. Sports Med. 2013, 34(6),497-501).

E. Device Validation

This study was approved by the Institutional Review Board at PurdueUniversity. As a pilot, we tested our device on two participants,Subject A and Subject B. Prior to participating in the study, bothsubjects gave oral and written consent. Both subjects were male in theage range of 25-34 years. Other physical characteristics of bothsubjects are summarized in Table 1 and each subject's skin tone is shownin FIG. 2D. Subjects were allowed to place the experimental device oneither hand in accordance with the location each subject usually wearswristwatches. Subjects were instructed to place the device snugly on thewrist, to their own comfort. The device was located about 1-2 inchesupwards from the wrist. Subject A placed the device on his non-dominanthand (left), while Subject B placed the device on his dominant hand(right). Each subject also wore the reference standard below the chestabout even-level with the diaphragm. Measurements were taken whileSubject A sat upright in a chair around a small desk. Subject B wasmonitored while lying flat on a couch, sleeping.

For initial benchtop validation, the discrete Fourier transform (DFT)was computed in Microsoft Excel using the “Data Analysis ToolPak”toolkit. The DFT was calculated with 128 samples using a rectangularwindow for both sets of measurements (heart rate and respiration). Wethen identified the local maxima of the resulting spectra and comparedthose frequencies to the respiratory and heart rates returned by thecommercial device.

TABLE 1 Physical Characteristics of Participating Subjects Subject ASubject B Age Range (years) 25-34 25-34 Gender Male Male Height (cm)175.3 172.7 Weight (kg) 87.1 106.6 Skin Tone Dark Medium Dominant HandRight Right Sensor Location Left Wrist Right Wrist Testing PositionSeated Upright Sleeping

The sampling rates and number of samples were chosen in order tooptimize frequency resolution as well as the length of sampling period.In order to collect 128 samples at 11.9 Hz for our heart ratemeasurements, it is necessary to sample for 11 seconds giving our heartrate determination a refresh rate of 11 seconds and a resolution of 0.09Hz (5.4 BPM). The frequencies of interest were limited to 0.7 to 3.5 Hz.The conditions for respiratory rate monitoring were determinedsimilarly. Sampling at 4.0 Hz for 128 samples provides a refresh rate of33 seconds and a frequency resolution of 0.03 Hz (1.8 BrthPM). Thefrequencies of interest were limited to 0.2 Hz to 0.5 Hz.

To properly compare the results from our device to the BioHarness, a fewspecial considerations had to be made. The reference device returnsbeat-to-beat heart rate and breath-to-breath respiratory ratemeasurements, while the experimental device returns the rates over therespective sampling periods. As a result, we averaged the rates reportedby the reference device over the same sampling periods as theexperimental device.

After benchtop validation, the on-board microcontroller was programmedto compute the DFT on-chip, allowing the device to be completelyportable and independent of a computer. The microcontroller wasprogrammed according to the expression for calculating the discreteFourier transform (DFT) using a rectangular window (J Allen, IEEE Trans.Acoust. Speech Signal Process, 1977, 25(3), 235-238). Care was taken toavoid performing trigonometric floating-point calculations on themicrocontroller as these are computationally expensive and severely slowdown our data processing. Instead, we stored the trigonometricrelationships in an array of 128 values mapped as 16-bit unsignedintegers between 0 and 1000. Indexing the array instead of computing theexact value of the trigonometric function increased our computing speedextensively. We then limited the calculation of the DFT to thefrequencies of interests, namely 0.7 Hz to 3.5 Hz for heart ratemeasurements and 0.2 Hz to 0.5 Hz for respiratory rate measurements. Wewere able to calculate the DFT in less than 50 ms compared to 83 secondswhen using the exact value of the trigonometric function.

Results and Discussion

Overall, the results from our initial pilot study showed reasonableagreement between the experimental device and the reference device. Foreach Subject, the error rates were within 3-4 BPM or BrthPM (accountingfor rounding) as indicated in FIGS. 3A-3F. For Subject A, theexperimental device reported prominent frequency content at 0.28 Hz and1.40 Hz for heart rate, and 0.12 Hz and 0.28 Hz for respiration. Thereference device reported rates of 80 BPM and 16.1 BrthPM during thesampling period, corresponding to 1.40 Hz (84 BPM) and 0.28 Hz (16.8BrthPM) in the data collected by the experimental device. Furthermore,we observed that for Subject A, the respiratory component could be seenin the heart rate signal (FIGS. 3A and 3B at 0.28 Hz) even beforefurther signal processing was done to amplify the respiratory signal.Data agreement was similar for Subject B. We observed frequency contentat 1.21 Hz in the heart rate data and 0.12 Hz and 0.25 Hz in therespiratory data. The reference device reported an average heart rate of73 BPM and an average respiratory rate of 15.3 BrthPM during thesampling period. This agrees with frequency content at 1.21 Hz (73 BPM)and 0.25 Hz (15 BrthPM) reported by the experimental device.

We do observe the presence of additional frequency content for eachSubject and for each measurement that do not appear to be indicative ofany physiological signal indicated by the reference device (0.12 Hz inrespiratory rate measurements for both subjects and 0.74 Hz in the heartrate spectra for Subject A). We postulate that these additionalfrequency components could be due to system baseline drift and furtherinvestigation is necessary to confirm.

Furthermore, we validated our device for untethered collection of datawith all processing and determination of physiological rates doneon-board (FIGS. 4A and 4B.). Our respiratory rate measurements agreedwithin 1 BrthPM of the reference standard, while the heart ratemeasurements agreed within 3 BPM.

To summarize, we have presented a proof-of-concept for accuratemeasurements of respiration and heart rate on the wrist with a singledevice. Our device improves upon other wrist-worn PPG sensors, which areonly capable of measuring heart rate alone, by demonstrating the abilityto detect respiration in addition to heart rate. Future optimization ofour algorithms will be done in order to improve the refresh rate of ourmeasurements. We postulate this could be done by sliding our DFTcomputation across the sampling period. We will also increase the numberof human subjects in order to validate the accuracy of our device forall-day wear.

FIG. 1 shows a prototype of a wearable device disclosed herein.

FIG. 2A depicts the waveforms (top panel) the respiration signal inreal-time. The dark blue signal is the signal from the gold standard(impedance pneumograph, also known as a pneumatograph or spirograph),the light blue is the signal from our custom device, and the pink signalis also from our custom device after further amplification andfiltering. The bottom graph is the fast Fourier Transform (FFT) of thegold standard. This is used to analyze the frequency components of thesignal collected from the gold standard in order to calculaterespiratory rate. There is a primary frequency component at around 300mHz. Meaning the respiratory rate is about 0.3 Hz*60=18 breaths perminute.

FIG. 2B shows the FFT analysis is now of our custom device. We arecomparing the bottom graph of this slide to the bottom graph of slide 3.As you note, there is a primary frequency component at around 300 mHz.This primary frequency component of our custom device (bottom graphslide 4) is identical to the primary frequency component of the goldstandard (bottom graph slide 3) meaning that our device was able todetect respiration as well as the gold standard.

FIG. 2C shows scaled up respiratory patterns as detected by the goldstandard (in dark blue), our device (in light blue), and our deviceagain after further amplification and filtering (pink). The sinusoidalpatterns measured by our device matches the gold standard for normalbreathing, short choppy breaths, and holding one's breath. This slidedemonstrates that our device is also able to detect breathing patternssimilar to the gold standard.

FIG. 3A shows the time domain response of the experimental device toheartbeats for Subjects A and B. We can visibly observe at least 2frequency components in each signal, one possibly corresponding to heartrate and the other to respiration.

FIG. 3B shows each signal is processed in the frequency domain.Suspected frequency components indicative of heart rate are highlighted.

FIG. 3C shows data from the reference device indicates heart rates of 83BPM and 73 BPM for Subjects A and B, respectively. These ratescorrespond to frequency components 1.40 Hz for Subject A and 1.21 Hz forSubject B.

FIG. 3D shows the respiratory signal is shown. We notice slower changesin the signal on the order of several seconds compared to heart rate,which was more on the order of I to 1.5 seconds.

FIG. 3E shows the spectra of the respiration signals are highlightedwith two dominant frequency components for each Subject (0.12 Hz and0.28 Hz for Subject A and 0.12 Hz and 0.25 Hz for Subject B).

FIG. 3F shows data from the reference device indicates a respiratoryrates of 16.1 BrthPM for Subject A and 15.3 Hz for Subject B. Theserates correspond to peaks at 0.28 Hz (Subject A) and 0.25 Hz (Subject B)for each respective subject.

FIG. 4A shows amplitude increase in signal due to activity of auto-gainfunction. The microcontroller samples the dataset for the full samplingperiod, then adjusts the gain of the active filters to module signalamplitude.

FIG. 4B shows real-time, on-board signal processing of heart andrespiratory signals after a very stressful event. The refresh rate ofthe display is such that heart rate (HR) and respiratory rate (RR) couldnot be captured in a single photograph at the same time.

Additionally, there is a few seconds delay between each snapshotresulting in the difference in HR and RR reported by the referencedevice (viewed on the smartphone), which has a faster refresh rate thanthe experimental device.

FIG. 5A shows our schematic for our biometric sensor board. In thisdesign, we depict our amplification and filtering scheme for measuringheart rate, respiration, and pulse oximetry. The mathematicalcalculation of oxygen saturation follows the methods described in theliterature as demonstrated with the following formulae shown below (SSassaroli, et al., Phys. Med. Biol. 2004, 49(14), N255; D. R. Tobler etal., U.S. Pat. No. 6,285,896, 4 Sep. 2001; J M Schmitt, IEEETransactions on Biomedical Engineering, 1991, 38(12), 1194-1203; W. G.Zijlstra, et al., Clinical Chemistry, 1991, 37(9), 1633-1638). We have adual LED (red and infrared) in which we are able to automaticallycontrol the brightness of the LEDs to account for difference in skintones.

$\; {{S(t)} = \frac{{AC}(t)}{{DC}(t)}}$${R(t)} = \frac{\ln \left( {{{rms}\left( {S(T)}_{R} \right)} + 1} \right)}{\ln \left( {{{rms}\left( {S(T)}_{IR} \right)} + 1} \right)}$${{SpO}_{2}(t)} = \frac{{ɛ_{Hb}\left( \lambda_{R} \right)}{DPF}_{R - {IR}}{ɛ_{Hb}\left( \lambda_{IR} \right)}{R(t)}}{{\left\lbrack {{ɛ_{Hb}\left( \lambda_{R} \right)} - {ɛ_{{HbO}_{2}}\left( \lambda_{R} \right)}} \right\rbrack {DPF}_{R - {IR}}} + {\left\lbrack {{ɛ_{{HbO}_{2}}\left( \lambda_{IR} \right)} - {ɛ_{Hb}\left( \lambda_{IR} \right)}} \right\rbrack {R(t)}}}$

The signal processing for heart rate and respiration follows four majorsteps: a). Discrete Fourier Transform (DFT); b) Peak Detector; c)Spectral Centroid; d) Segmentation.

Discrete Fourier Transform (DFT)

The DFT calculates the frequency content of our signal (R M RangayyanBiomedical Signal Analysis. John Wiley & Sons, 2015; J W Cooley, et al.,IEEE Transactions on Education, 1969, 12(1), 27-34). This calculationshows how much each frequency contributes to the overall signal bycalculating the “magnitude” at each frequency which is a mathematicalweight representing how much a particular frequency contributes to theoverall signal. We calculate the DFT for our physiological signal (heartrate or respiratory rate) as well as the signals from the accelerometerand gyroscope.

${X(k)} = {\sum\limits_{k = 0}^{N - 1}{{x(n)}*\left\lbrack {{\cos \left( {\frac{2\pi}{N}kn} \right)} - {j*\sin \; \left( {\frac{2\pi}{N}{kn}} \right)}} \right\rbrack}}$

where x(n) are the data samples and k=0 to N−1

Peak Detector.

We then employ a peak detector which finds the given frequency that hasthe largest contribution to the signal by comparing the magnitudes ofthe DFT calculation at each frequency. We also find the peak frequencyin the acceleration and gyroscope data as well. The software thencompares the peaks found in the physiological signal to the peaks foundin the accelerometer and gyroscope. If the peaks found in thephysiological signal are also found in the accelerometer and gyroscope,the frequency is considered noise. The software does this until a uniquefrequency in the physiological signal that is not represented in theaccelerometer or gyroscope data is found.

Spectral Centroid

Once a unique frequency is found for the physiological signal, wecalculate the spectral centroid. This is a weighted average of thefrequency spectrum around our unique frequency. This allows us to findsmall variances in our frequency analysis that could be due to smallcontributions by frequencies around our unique frequency (G J Sandell,Music Percept, 1995, 13(2), 209-246; P N Le, et al., SpeechCommunication, 2011, 53(4), 540-551; K A Wear, IEEE Transactions onUltrasonics, Ferroelectrics, and Frequency Control, 2003, 50(4),402-407).

${Centroid} = {\sum\limits_{k = 0}^{N - 1}\frac{f_{k}{X(k)}}{X(k)}}$

wherein, X(k) is the magnitude of the DFT for each frequency, and f, kare each individual frequency.

The Centroid value represents the most accurate frequency indicative ofour physiological value, whether it be heart rate or respiration. Tocalculate heart rate or respiratory rate, we multiply the centroid by 60to obtain breaths per minute or beats per minute (depending on whichphysiological signal we are analyzing)

Centroid*60=breaths per minute or beats per minute

Segmentation.

The software then re-analyzes the original physiological signal to intodifferent segments. For instance, if we have N samples, the softwarefinds N/4 or N/2 samples that have less noise in them than the entiresignal itself. The software does this by observing the level of activityreported by the accelerometer and gyroscope. The software tries to finda period of time within the N samples where the level of activityreported by the accelerometer and gyroscope are low. Once the data issegmented into N/4 or N/2 samples, the software analyzes the segmenteddata as described above. By doing so, we can perform more accuratefrequency analysis by excluding data samples that are too obscured bynoise.

A computer program in a computer-readable form (CRF) is submittedconcurrently with this application. The file, entitled68015-02_computer_listing.txt, is generated on Sep. 5, 2018. The contentof the computer listing is hereby incorporated by reference in itsentirety. The program carries out some of the data collection,mathematic calculations, data analysis, readout and sharing.

FIG. 5B shows schematic of the PPG sensing circuit. The signal from thetransimpedance amplifier is sent into two sets of cascaded activefilters tuned for heart rate sensing and respiration monitoringrespectively. The outputs of the amplifier stages are sensed by twochannels of an on-board IO-bit analog-to-digital converter on aBluetooth-enabled microcontroller. The signals are processed by theon-board microcontroller. R3, R14, and R15 (highlighted with a dashedbox) are digitally controlled potentiometers enabling automatic gaincontrol. Using these potentiometers, the microcontroller can modulatesystem gain in order to adjust for differences in the optical reflectiveproperties of skin across difference subjects.

FIG. 6 depicts the circuit board design for our biometric sensor boardshowing how the circuit board is physically laid out in space. Along theleft side are the amplifiers and filters. At the center and bottom ofthe board are the LEDs and light sensor. At the top is the brightnesscontrol mechanism for the LEDs.

FIG. 7 shows the back side of the circuit board of FIG. 6

FIG. 8 shows the auxiliary sensor board that contains battery managementsystems (voltage regulation, battery charging, battery status), alocomotion sensor (accelerometer and gyroscope), a real-time clock (foraccurate time keeping), a motor (for user feedback), and a buzzer (alsofor user tactile feedback).

FIG. 9 shows the circuit board layout for the auxiliary sensor board andwhere all the components are laid out.

FIG. 10 shows the communications and central processing board thatcontains our Bluetooth capable central processing unit, an externalmemory chip (RAM), a screen for displaying information to the user(OLED), an SD card for long term data storage, and buttons for userinput. This board handles all the data processing to measure heart rate,respiratory rate, and pulse oximetry. This board is also responsible forcontrolling all other boards and for sending data to the user.

FIG. 11A depicts the circuit board layout for our communications andcentral processing board. FIG. 11B shows the back side of the circuitboard.

It will be appreciated by persons skilled in the art that the presentinvention is not limited by what has been particularly shown anddescribed herein. The details of one or more embodiments of theinvention are set forth in the accompanying the description below. Otherfeatures, objects, and advantages of the invention will be apparent fromthe description and drawings, and from the claims.

Those skilled in the art will recognize that numerous modifications canbe made to the specific implementations described above. Theimplementations should not be limited to the particular limitationsdescribed. Other implementations may be possible.

While the inventions have been illustrated and described in detail inthe drawings and foregoing description, the same is to be considered asillustrative and not restrictive in character, it being understood thatonly certain embodiments have been shown and described and that allchanges and modifications that come within the spirit of the inventionare desired to be protected. It is intended that the scope of thepresent methods and apparatuses be defined by the following claims.However, it must be understood that this disclosure may be practicedotherwise than is specifically explained and illustrated withoutdeparting fromits spirit or scope. It should be understood by thoseskilled in the art that various alternatives to the embodimentsdescribed herein may be employed in practicing the claims withoutdeparting from the spirit and scope as defined in the following claims.

1. A method for measuring and monitoring heart rate, respiration, and oxygen saturation of a subject with a single device comprising the step of a) measuring reflectance of red and infrared lights off skin tissue using a biometric sensor board; b) processing and analyzing data collected in step a); and c) displaying and/or sharing results of the processed data remotely.
 2. The method of claim 1, wherein the subject is a human patient or a healthy subject in need of monitoring for safety purpose.
 3. The method of claim 1, wherein said device is a wrist-worn device.
 4. The method of claim 1, wherein said biometric sensor board extracts and collects photoplethysmographic data from said subject.
 5. The method of claim 1, wherein said biometric sensor board comprises a light sensor, a dual wavelength light source (red and infrared) with an auto-gain feedback loop for calibrating skin tones and pulse strength, and a plurality of active filters for an enhanced signal to noise ratio with an auto-gain feedback loop to maintain a signal strength for signal processing.
 6. The method of claim 1, wherein said data processing and analyzing carried out by a microcontroller equipped with a central processing unit comprises noises elimination from extracted photoplethysmographic data using a motion tracking sensor for an enhanced signal to noise ratio.
 7. The method of claim 6, wherein said motion tracking sensor is an accelerometer or a gyroscope.
 8. The method of claim 1, wherein said data processing and analyzing comprises a frequency analysis using a Fourier Transform on data collected over several seconds of time, wherein said frequency is related to a specific biometric measurement of heart rate and respiration.
 9. The method of claim 8, wherein said frequency for heart rate measurement ranges from about 0.4 Hertz (Hz) to about 4 Hz; said frequency for respiration measurement ranges from about 0.05 Hz to about 0.5 Hz; and said heart rate and respiration are determined by comparing the magnitude of the peaks of the Fourier Transform for heart rate and respiration at each frequency to the magnitude of the peaks in the Fourier Transform of the accelerometer/gyroscope data in all three axes at each frequency.
 10. The method of claim 1, wherein said oxygen saturation in the blood of said subject is determined by comparing the relative signal strength of red and infrared light collected over several seconds of time.
 11. A method to measure heart rate and respiration saturation of a subject with a single device comprising the step of: (a) measuring reflectance of red and infrared lights off skin tissue using a biometric sensor board; (b) processing and analyzing data collected in step (a), wherein the processing and analyzing data includes: (i) sampling the time-varying photosensor signal and the time-varying motion signals to thereby generate a digitized time-varying photosensor signal and digitized time-varying motion sensor signals, (ii) applying a Fourier transform to the digitized time-varying photosensor signal and the digitized time-varying motion signals to thereby generate frequency domain spectra associated with magnitudes of the digitized time-varying photosensor signal and the digitized time-varying motions signals, (iii) detecting peaks of the magnitudes of the spectra of the digitized time-varying photosensor signal and the digitized time-varying motions signals, (iv) comparing the peaks of magnitudes of the frequency domain spectrum associated with the digitized time-varying photosensor signal with the peaks of magnitudes of the frequency domain spectrum associated with the digitized time-varying motion signals, (v) identifying a peak present in the frequency domain spectrum associated with the digitized time-varying photosensor signal which is not present in the peaks of magnitudes of the frequency domain spectrum associated with the digitized time-varying motion signals, and (vi) multiplying the frequency associated with the identified peak by 60 to thereby generate activity per minute; and (c) displaying and/or sharing results of the processed data remotely.
 12. The method of claim 11, wherein the subject is a human patient or a healthy subject in need of monitoring for safety purpose.
 13. The method of claim 11, wherein said device is a wrist-worn device.
 14. The method of claim 11, wherein said biometric sensor board extracts and collects photoplethysmographic data from said subject.
 15. The method of claim 11, wherein said biometric sensor board comprises a light sensor, a dual wavelength light source (red and infrared) with an auto-gain feedback loop for calibrating skin tones and pulse strength, and a plurality of active filters for an enhanced signal to noise ratio with an auto-gain feedback loop to maintain a signal strength for signal processing.
 16. The method of claim 11, wherein said data processing and analyzing carried out by a microcontroller equipped with a central processing unit comprises noises elimination from extracted photoplethysmographic data using a motion tracking sensor for an enhanced signal to noise ratio.
 17. The method of claim 16, wherein said motion tracking sensor is an accelerometer or a gyroscope.
 18. The method of claim 11, wherein said Fourier transform is performed on data collected over several seconds of time, wherein said frequency is related to a specific biometric measurement of heart rate and respiration.
 19. The method of claim 18, wherein said frequency for heart rate measurement ranges from about 0.4 Hertz (Hz) to about 4 Hz; said frequency for respiration measurement ranges from about 0.05 Hz to about 0.5 Hz; and said heart rate and respiration are determined by comparing the magnitude of the peaks of the Fourier Transform for heart rate and respiration at each frequency to the magnitude of the peaks in the Fourier Transform of the accelerometer/gyroscope data in all three axes at each frequency.
 20. The method of claim 11, wherein said oxygen saturation in the blood of said subject is determined by comparing the relative signal strength of red and infrared light collected over several seconds of time. 